# Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.1 Evaluation of Screening and Diagnostic Tests.

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Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.1 Evaluation of Screening and Diagnostic Tests

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.2 Ask Marilyn ® BY MARILYN VOS SAVANT Potential employees and athletes are often tested for drug use. Suppose it is assumed that about 5% of the general population uses drugs. You employ a test that is 95% accurate, which we’ll say means that if the individual is a user, the test will be positive 95% of the time, and if the individual is a nonuser, the test will be negative 95% of the time. A person is selected at random and is given the test. It’s positive. What does such a result suggest? Would you conclude that the individual is a drug user? What is the probability that the person is a drug user?

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.3 Diagnostic Tests  Important part of medical decision making  In practice, many tests are used to obtain diagnoses  There is a need to evaluate these tests (for accuracy and overall value)

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.4 Example  Pap smear – screening test for cancer of the cervix  When a Pap smear is positive, abnormal cells are present approximately 95% of the time (positive predictive value).  When a Pap smear is negative, there can still be abnormal cells missed by the test. The test may miss abnormal cells 30% of the time (false negative).

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.5 Example  Prostate cancer screening  Two primary tests  digital rectal examination (DRE)  prostate specific antigen (PSA) test  Positive Predictive Value = 20% for men > 50

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.6 Performance Characteristics  How do we evaluate a screening or diagnostic test?  How well does the test identify patients with a disease?  How well does the test identify patients without a disease?  Once a screening test result is obtained, what is the likelihood that it is correct?

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.7 Why do we screen?  Reduced cost  Resource efficiency  Speed and timeliness of results  Simplicity  Adaptability to a variety of clinical settings  Lack of invasiveness  Acceptability in the population being evaluated  Practicality to administer to many people

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.8 Evaluation is Relative  The performance of a screening test should be taken in context of these factors.  Consideration of alternatives (e.g., other screening tests or performing a complete exam) should also be considered.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.9 S A B S=Sample space (totality of all events)

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.10 Probability  The concept: If a trial (or experiment) is independently repeated a large number of times (N) and an outcome (A) occurs n times, then:  P(A) = n/N  Interpretation: if the trial is repeated again in the future, the likelihood that A will be the outcome again is n/N.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.11 Introductory Probability  Venn Diagrams  Draw them. Useful for thinking about probability  Definitions  Intersection (A ∩ B)  Union (A U B)  Complement (A C )  Ø A null event: Cannot happen  Mutually exclusive events implies no intersection  Example: (A ∩ A C ) = Ø by definition

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.12 Probability  In general: 0 ≤ P(A) ≤ 1  Sample space:P(S)=1  Impossible (null event):P(A)=0  Sure thing:P(A)=1

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.13 Probability  For any two events:  P(A U B)=P(A)+P(B)-P(A ∩ B)  If A and B are mutually exclusive, then P(A ∩ B)=0, and thus  P(A U B)=P(A)+P(B)  P(A C )=1-P(A)

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.14 Conditional Probability  The probability of an event (B) occurring conditional (or given) that event A occurs is:  P(B|A)=[P(A ∩ B)] / P(A)  Two event (A and B) are independent if P(B|A)=P(B) (i.e, knowledge of A does not affect the probability of B)  Thus in this case, P(A ∩ B)=P(A)P(B)  Example: drawing cards w/ and w/o replacement

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.15 Conditional Probability  Sensitivity, specificity, and predictive values (which are all used to evaluate screening/diagnostic tests) are all conditional probabilities.  Note: a p-value is a conditional probability too.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.16 Evaluation of a Screening Test 1.Give a group of people both tests (the screening test and the “gold standard” test (e.g., a full and complete exam that will diagnose disease without error) 2.Cross-classify the results

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.17 True Disease +- Screening Test + a c (false positive) - b (false negative) d

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.18 Performance Characteristics  Sensitivity = P(T + |D + ) = a/(a+b)  Specificity = P(T - |D - ) = d/(c+d)  A perfect test would have b and c equal to 0.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.19 Performance Characteristics  False positive rate = P(T + |D - ) = c/(c+d) = 1 – Specificity  False negative rate = P(T - |D + ) = b/(a+b) = 1 – Sensitivity

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.20 Predictive Values  More informative from the patient or physician perspective  Special applications of Bayes Theorem

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.21 Predictive Values  Positive Predictive Value = P(D + |T + ) = = a/(a+c) if the prevalence of disease in the general population is the same as the prevalence of disease observed in the study

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.22 Predictive Values  Negative Predictive Value = P(D - |T - ) = = d/(b+d) if the prevalence of disease in the general population is the same as the prevalence of disease observed in the study

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.23 Answering Marilyn’s Question  Insert Marilyn_Example

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.24 Example: X-ray Screening for Tuberculosis  Tuberculosis  infectious bacterial disease usually affecting the lungs  X-rays often used as a screening tool  PCR test can be performed to check for the presence of TB bacteria in the blood or other body fluids.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.25 Example: X-ray Screening for Tuberculosis  Insert TB_Example

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.26 Example: BPNS in NARC 007  NARC 007 – Evaluation of the Brief Peripheral Neuropathy Screen (BPNS) vs. a comprehensive neurologic evaluation using the Total Neuropathy Score (TNS) for distal sensory-predominant neuropathy (DSPN)  Insert neuropathy_example

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.27 ROC Curves  In some scenarios, the screening or diagnostic test is measured on a continuous scale and a “cutpoint” is used to categorize a patient as diseased or non-diseased.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.28 ROC Curves  For example, a test for a disease may be graded on a scale from 0 to 100. If the subject scores above 70, then the subject is characterized as diseased (i.e., a score below 70 indicates non-diseased).

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.29 ROC Curves  In order to improve sensitivity, we may consider moving the cutpoint from 70 to 60. This will increase the number of “positives”, and thus increase sensitivity.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.30 ROC Curves  Note, however there is a cost for this. The false positive rate (i.e., the probability that the test is positive when the subject is truly non-diseased) also increases. Thus specificity decreases.  Thus, in general, as sensitivity increases, specificity decreases.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.31 ROC Curves  An ROC curve plots sensitivity versus (1-specificity) so that the trade-off can be visualized as the cutpoint varies. The original 2X2 table represents a single point on the ROC plot.

Introduction to Biostatistics, Harvard Extension School © Scott Evans, Ph.D.32 Example: BNCS in NARC 007  NARC 007 – Evaluation of the Brief Neuro-Cognitive Screen (BNCS) vs. a comprehensive neuropsychological exam for neurocognitive impairment  Insert ROC_example  See NARC_007_final.pdf

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